Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study

Lo, Chun-Chih; Tsai, Shang-Ho; Lin, Bor‐Shyh

doi:10.3390/en9020113

Cited by 39 publications

(17 citation statements)

References 20 publications

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“…The cold storage is an important interface between the central air conditioning cold source and the chilled water or heat transfer of indoor air . The chilled water transportation cold source system transports chilled water to each floor of the air conditioning unit through a chilled water pump and then mixes the air with the cooling water and fan in the cooling device to perform heat and moisture exchange in the surface cooler.…”

Section: Power Saving Optimization Methods Of Central Air Conditioningmentioning

confidence: 99%

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Jiao

2019

Int Trans Electr Energ Syst

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Summary Owing to the large power consumption and poor indoor comfort of central air conditioning, controlling the central air conditioning for cool storage and analyzing the power saving optimization of the central air conditioning operation have become necessary. Therefore, a method of power saving optimization analysis based on an improved ant colony air conditioning system is proposed to solve the problem of power consumption. According to the characteristics of cold storage operation regulation, the power consumption of chilled water pumps and air conditioning fans were used to establish a model, and the influence of cold storage operation regulation on central air conditioning operation power consumption was analyzed. On this basis, the improved ant colony is used to optimize the power saving path, according to the factors affecting the power consumption of central air conditioning operation above, and the optimal operation of air conditioning and storage regulation was analyzed, and the power saving treatment of central air conditioning operation was completed. The experimental results show that when the method is adopted, power consumption can be reduced by 67 560 kWh for the entire year, the total consumption of the optimal working point is approximately 0.384 kW, and the power saving effect is better with certain advantages.

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Section: Power Saving Optimization Methods Of Central Air Conditioningmentioning

confidence: 99%

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Jiao

2019

Int Trans Electr Energ Syst

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“…Another area of research on cooling control optimization is to optimize the ice-storage system [24] [25], so that it can be used for cooling when the electricity price is high. In addition to directly optimizing the cooling system surveyed above, there exists an extensive body of works that focus on the IT side of the DC to save cooling energy.…”

Section: A Recent Progress On DC Cooling Optimizationmentioning

confidence: 99%

Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Wen

Tao

et al. 2020

IEEE Trans. Cybern.

149

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Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when given the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.

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“…In order to evaluate the impact of demand response programs on the economic dispatch, a model of elastic loads combining with the dynamic incentive mechanism is proposed here [30][31][32]. With variable changes of incentive on the time scale, customers will move the peak load to fill the off-peak and valley periods actively, ensuring security of power system during the peak time.…”

Section: Modeling Of Demand Responsementioning

confidence: 99%

An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability

Ding

Zheng

2017

Sustainability

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Demand response (DR) has become an impressive option in the deregulated power system due to its features of availability, quickness and applicability. In this paper, a novel economic dispatch model integrated with wind power is proposed, where incentive-based DR and reliability measures are taken into account. Compared with the conventional models, the proposed model considers customers' power consumption response to the incentive price. The load profile is optimized with DR to depress the influence on the dispatch caused by the anti-peak-shaving and intermittence of wind generation. Furthermore, a probabilistic formulation is established to calculate the expected energy not supplied (EENS). This approach combines the probability distribution of the forecast errors of load and wind power, as well as the outage replacement rates of units into consideration. The cost of EENS is added into the objective to achieve an optimal equilibrium point between economy and reliability of power system operation. The proposed model is solved by mixed integer linear programming (MILP). The applicability and effectiveness of this model is illustrated by numerical simulations tested on the IEEE 24-bus Reliability Test System. Sustainability 2017, 9, 758 2 of 20 with a certain proportion of the forecast load or the maximum capacity of the operating units. This deterministic means has left out multiple uncertainties, such as the wind forecast error, the load forecast error, and the forced outage rate of units [12,13]. During recent years, the stochastic assessment of the spinning reserve has been applied in many articles [14][15][16]. With this approach, the spinning reserve is optimized to satisfy the expected energy not supplied (EENS). In [17], a triangular approximate distribution model is used to quantify EENS due to the stochastic feature of wind. Then, a security-constrained unit commitment algorithm is proposed to schedule conventional units and wind generation considering probabilistic forecast models of wind power. Another day-head scheduling model involving reliability criteria is described in [18]. EENS is calculated with the historical outage replacement rates of generators or lines. MILP is utilized to deal with the proposed model. In [19], the forecast errors of both wind power and load are supposed to follow normal distributions. These errors are discretized into several intervals for a new formulation of reliability measures. In [20,21], a two-stage stochastic problem is formulated to address various uncertainties in the system, such as wind power, solar generation, loads and even electric vehicles. The sample average approximation, which is a Monte Carlo simulation technique, is utilized to deal with uncertainties for scenario generation purposes. These works have integrated the system with distribution-free uncertainties, but given a rise to the computational burden.As an effective way for peak load shedding and shaving, DR has been widely investigated in the electricity market. Consuming wind power with DR will re...

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Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study

Cited by 39 publications

References 20 publications

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability

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